{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set_context(rc={'figure.figsize': (14, 7) } )\n",
    "figzize_me = figsize =(14, 7)\n",
    "\n",
    "import os\n",
    "import sys\n",
    "# 使用insert 0即只使用github，避免交叉使用了pip安装的abupy，导致的版本不一致问题\n",
    "sys.path.insert(0, os.path.abspath('../'))\n",
    "import abupy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 因为需要全市场回测所以本章无法使用沙盒数据，《量化交易之路》中的原始示例使用的是美股市场，这里的示例改为使用A股市场。\n",
    "* 本节建议对照阅读abu量化文档第20-23节内容\n",
    "* 本节的基础是在abu量化文档中第20节内容完成运行后有A股训练集交易和A股测试集交易数据之后\n",
    "\n",
    "[abu量化系统github地址](https://github.com/bbfamily/abu) (您的star是我的动力！)\n",
    "\n",
    "[abu量化文档教程ipython notebook](https://github.com/bbfamily/abu/tree/master/abupy_lecture)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第11章 量化系统-机器学习•ABU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "disable example env\n"
     ]
    }
   ],
   "source": [
    "from abupy import AbuFactorAtrNStop, AbuFactorPreAtrNStop, AbuFactorCloseAtrNStop, AbuFactorBuyBreak\n",
    "from abupy import abu, EMarketTargetType, AbuMetricsBase, ABuMarketDrawing, ABuProgress, ABuSymbolPd\n",
    "from abupy import EMarketTargetType, EDataCacheType, EMarketSourceType, EMarketDataFetchMode, EStoreAbu, AbuUmpMainMul\n",
    "from abupy import AbuUmpMainDeg, AbuUmpMainJump, AbuUmpMainPrice, AbuUmpMainWave, feature, AbuFeatureDegExtend\n",
    "from abupy import AbuUmpEdgeDeg, AbuUmpEdgePrice, AbuUmpEdgeWave, AbuUmpEdgeFull, AbuUmpEdgeMul, AbuUmpEegeDegExtend\n",
    "from abupy import AbuUmpMainDegExtend, ump, Parallel, delayed, AbuMulPidProgress, AbuProgress\n",
    ",\n",
    "# 关闭沙盒数据\n",
    "abupy.env.disable_example_env_ipython()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集结果：\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "买入后卖出的交易数量:89418\n",
      "买入后尚未卖出的交易数量:2012\n",
      "胜率:46.5220%\n",
      "平均获利期望:8.4475%\n",
      "平均亏损期望:-5.6229%\n",
      "盈亏比:1.3312\n",
      "策略收益: 61.3978%\n",
      "基准收益: 111.5646%\n",
      "策略年化收益: 12.2991%\n",
      "基准年化收益: 22.3484%\n",
      "策略买入成交比例:31.7084%\n",
      "策略资金利用率比例:88.6240%\n",
      "策略共执行1258个交易日\n"
     ]
    },
    {
     "data": {
      "image/png": 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RQxNgjnwJTBEREREROebUNAbXbLn36/OYPjGFq84sBCAmysLps3J6PSclIarPANPYGgw6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\n8SkKrTOx6Mz84ZPHOWavoKS9lI3H36DV2Y5erZMLdiO/43WZq8Z8nuu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KjZoTp4F19rhJ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\nGcZf9vwNZxrUlMbws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      "text/plain": [
       "<matplotlib.figure.Figure at 0x18fb3f198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集结果：\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "买入后卖出的交易数量:6321\n",
      "买入后尚未卖出的交易数量:124\n",
      "胜率:47.6507%\n",
      "平均获利期望:15.7211%\n",
      "平均亏损期望:-7.6456%\n",
      "盈亏比:2.0477\n",
      "策略收益: 102.4382%\n",
      "基准收益: 52.5454%\n",
      "策略年化收益: 21.2639%\n",
      "基准年化收益: 10.9073%\n",
      "策略买入成交比例:26.2064%\n",
      "策略资金利用率比例:73.9138%\n",
      "策略共执行1214个交易日\n"
     ]
    },
    {
     "data": {
      "image/png": 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KtkO9btfk7gxsIpmTZKlsOxy9Xe2q5YND67hvy0M8u/fl6OOxgaKqDP4099zZ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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x184c3a898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "abupy.env.g_market_target = EMarketTargetType.E_MARKET_TARGET_CN\n",
    "abupy.env.g_data_fetch_mode = EMarketDataFetchMode.E_DATA_FETCH_FORCE_LOCAL\n",
    "abu_result_tuple_train = abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME, \n",
    "                                             custom_name='train_cn')\n",
    "abu_result_tuple_test = abu.load_abu_result_tuple(n_folds=5, store_type=EStoreAbu.E_STORE_CUSTOM_NAME, \n",
    "                                             custom_name='test_cn')\n",
    "ABuProgress.clear_output()\n",
    "print('训练集结果：')\n",
    "metrics_train = AbuMetricsBase.show_general(*abu_result_tuple_train, only_show_returns=True)\n",
    "print('测试集结果：')\n",
    "metrics_test  = AbuMetricsBase.show_general(*abu_result_tuple_test, only_show_returns=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11.1 搜索引擎与量化交易\n",
    "\n",
    "* 本节建议对照abu量化文档第15节内容进行阅读"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "orders_pd_train = abu_result_tuple_train.orders_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 选择失败的前20笔交易绘制交易快照\n",
    "# 这里只是示例，实战中根据需要挑选，rank或者其他方式\n",
    "plot_simple = orders_pd_train[orders_pd_train.profit_cg < 0][:20]\n",
    "# save=True保存在本地， 文件保存在~/abu/data/save_png/中\n",
    "ABuMarketDrawing.plot_candle_from_order(plot_simple, save=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11.2 主裁\n",
    "\n",
    "### 11.2.1 角度主裁\n",
    "\n",
    "\n",
    "请对照阅读ABU量化系统使用文档 ：第15节 中相关内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>buy_deg_ang42</th>\n",
       "      <th>buy_deg_ang252</th>\n",
       "      <th>buy_deg_ang60</th>\n",
       "      <th>buy_deg_ang21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>3.351</td>\n",
       "      <td>18.582</td>\n",
       "      <td>9.453</td>\n",
       "      <td>0.235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>1.917</td>\n",
       "      <td>21.440</td>\n",
       "      <td>7.386</td>\n",
       "      <td>2.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>26.084</td>\n",
       "      <td>-24.631</td>\n",
       "      <td>23.113</td>\n",
       "      <td>8.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>1</td>\n",
       "      <td>2.561</td>\n",
       "      <td>18.982</td>\n",
       "      <td>5.616</td>\n",
       "      <td>3.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>1.397</td>\n",
       "      <td>-14.568</td>\n",
       "      <td>2.074</td>\n",
       "      <td>1.519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            result  buy_deg_ang42  buy_deg_ang252  buy_deg_ang60  \\\n",
       "2012-10-12       0          3.351          18.582          9.453   \n",
       "2012-10-12       0          1.917          21.440          7.386   \n",
       "2012-10-12       0         26.084         -24.631         23.113   \n",
       "2012-10-12       1          2.561          18.982          5.616   \n",
       "2012-10-12       0          1.397         -14.568          2.074   \n",
       "\n",
       "            buy_deg_ang21  \n",
       "2012-10-12          0.235  \n",
       "2012-10-12          2.890  \n",
       "2012-10-12          8.784  \n",
       "2012-10-12          3.106  \n",
       "2012-10-12          1.519  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from abupy import AbuUmpMainDeg\n",
    "# 参数为orders_pd\n",
    "ump_deg = AbuUmpMainDeg(orders_pd_train)\n",
    "# df即由之前ump_main_make_xy生成的类df，表11-1所示\n",
    "ump_deg.fiter.df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "耗时操作，快的电脑大概几分钟，具体根据电脑性能，cpu数量，启动多进程进行训练："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pid:21290 gmm fit:100.0%\n",
      "pid:21288 done!\n",
      "pid:21287 done!\n",
      "pid:21291 done!\n",
      "pid:21290 done!\n",
      "pid:21289 done!\n"
     ]
    },
    {
     "data": {
      "image/png": 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IBz+G5qzD04bgqwMLHU2nvE52RnWVrr/AGxLwi0UB3t+h4/hwaIXLNVOTjCzu3pf/1V9O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\nQEMQBEEQhHbz/1tu9xXD2T9WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15ae9c5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VgJQZofUJQyU215OYr5LYr4NGOH82yhnE0bfJ9fUor+97RGkjnPcIA0+zmY6YMdganOvt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\nb0EvaQAI3Knt8fE4hlGfxG23fBLcTYe9cq2ryQnnr2OhUVJ+8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2afexDPPvt0V34GB2GR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\nZEAgkUgkEokEGRBIJBKJRCIB/n/NVWkMnQwQlAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d184160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = ump_deg.fit(brust_min=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lcs</th>\n",
       "      <th>lms</th>\n",
       "      <th>lps</th>\n",
       "      <th>lrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54_32</th>\n",
       "      <td>365</td>\n",
       "      <td>-0.0525</td>\n",
       "      <td>-19.1550</td>\n",
       "      <td>0.7096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54_35</th>\n",
       "      <td>629</td>\n",
       "      <td>-0.0248</td>\n",
       "      <td>-15.6015</td>\n",
       "      <td>0.6979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54_38</th>\n",
       "      <td>947</td>\n",
       "      <td>-0.0195</td>\n",
       "      <td>-18.5037</td>\n",
       "      <td>0.6674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54_47</th>\n",
       "      <td>532</td>\n",
       "      <td>-0.0417</td>\n",
       "      <td>-22.1767</td>\n",
       "      <td>0.6579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54_48</th>\n",
       "      <td>663</td>\n",
       "      <td>-0.0184</td>\n",
       "      <td>-12.2099</td>\n",
       "      <td>0.6682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54_53</th>\n",
       "      <td>796</td>\n",
       "      <td>-0.0335</td>\n",
       "      <td>-26.6577</td>\n",
       "      <td>0.7211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55_1</th>\n",
       "      <td>583</td>\n",
       "      <td>-0.0270</td>\n",
       "      <td>-15.7313</td>\n",
       "      <td>0.6998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55_8</th>\n",
       "      <td>1246</td>\n",
       "      <td>-0.0214</td>\n",
       "      <td>-26.6566</td>\n",
       "      <td>0.6790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55_18</th>\n",
       "      <td>429</td>\n",
       "      <td>-0.0209</td>\n",
       "      <td>-8.9820</td>\n",
       "      <td>0.6667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55_23</th>\n",
       "      <td>1054</td>\n",
       "      <td>-0.0168</td>\n",
       "      <td>-17.7548</td>\n",
       "      <td>0.6689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92_57</th>\n",
       "      <td>172</td>\n",
       "      <td>-0.0222</td>\n",
       "      <td>-3.8194</td>\n",
       "      <td>0.6802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92_59</th>\n",
       "      <td>245</td>\n",
       "      <td>-0.0377</td>\n",
       "      <td>-9.2387</td>\n",
       "      <td>0.7265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93_1</th>\n",
       "      <td>546</td>\n",
       "      <td>-0.0239</td>\n",
       "      <td>-13.0463</td>\n",
       "      <td>0.6703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93_8</th>\n",
       "      <td>850</td>\n",
       "      <td>-0.0230</td>\n",
       "      <td>-19.5334</td>\n",
       "      <td>0.6812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93_23</th>\n",
       "      <td>563</td>\n",
       "      <td>-0.0238</td>\n",
       "      <td>-13.3827</td>\n",
       "      <td>0.6980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93_40</th>\n",
       "      <td>621</td>\n",
       "      <td>-0.0146</td>\n",
       "      <td>-9.0912</td>\n",
       "      <td>0.6651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93_42</th>\n",
       "      <td>259</td>\n",
       "      <td>-0.0401</td>\n",
       "      <td>-10.3983</td>\n",
       "      <td>0.7413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93_51</th>\n",
       "      <td>305</td>\n",
       "      <td>-0.0785</td>\n",
       "      <td>-23.9460</td>\n",
       "      <td>0.7279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93_57</th>\n",
       "      <td>208</td>\n",
       "      <td>-0.0297</td>\n",
       "      <td>-6.1840</td>\n",
       "      <td>0.6731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93_59</th>\n",
       "      <td>255</td>\n",
       "      <td>-0.0382</td>\n",
       "      <td>-9.7367</td>\n",
       "      <td>0.7333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>368 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        lcs     lms      lps     lrs\n",
       "54_32   365 -0.0525 -19.1550  0.7096\n",
       "54_35   629 -0.0248 -15.6015  0.6979\n",
       "54_38   947 -0.0195 -18.5037  0.6674\n",
       "54_47   532 -0.0417 -22.1767  0.6579\n",
       "54_48   663 -0.0184 -12.2099  0.6682\n",
       "54_53   796 -0.0335 -26.6577  0.7211\n",
       "55_1    583 -0.0270 -15.7313  0.6998\n",
       "55_8   1246 -0.0214 -26.6566  0.6790\n",
       "55_18   429 -0.0209  -8.9820  0.6667\n",
       "55_23  1054 -0.0168 -17.7548  0.6689\n",
       "...     ...     ...      ...     ...\n",
       "92_57   172 -0.0222  -3.8194  0.6802\n",
       "92_59   245 -0.0377  -9.2387  0.7265\n",
       "93_1    546 -0.0239 -13.0463  0.6703\n",
       "93_8    850 -0.0230 -19.5334  0.6812\n",
       "93_23   563 -0.0238 -13.3827  0.6980\n",
       "93_40   621 -0.0146  -9.0912  0.6651\n",
       "93_42   259 -0.0401 -10.3983  0.7413\n",
       "93_51   305 -0.0785 -23.9460  0.7279\n",
       "93_57   208 -0.0297  -6.1840  0.6731\n",
       "93_59   255 -0.0382  -9.7367  0.7333\n",
       "\n",
       "[368 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ump_deg.cprs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "失败概率最大的分类簇62_47, 失败率为77.61%, 簇交易总数67.0, 簇平均交易获利-25.07%\n"
     ]
    }
   ],
   "source": [
    "max_failed_cluster = ump_deg.cprs.loc[ump_deg.cprs.lrs.argmax()]\n",
    "print('失败概率最大的分类簇{0}, 失败率为{1:.2f}%, 簇交易总数{2}, ' \\\n",
    "      '簇平均交易获利{3:.2f}%'.format(ump_deg.cprs.lrs.argmax(),\n",
    "                               max_failed_cluster.lrs * 100,\n",
    "                               max_failed_cluster.lcs,\n",
    "                               max_failed_cluster.lms * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62\n"
     ]
    },
    {
     "data": {
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rAAAAXRNcAQAA6JrgCgAAQNcEVwAAALomuAIAANA1wRUAAICuCa4AAAB0TXAFAACga1vm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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1384dd7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cpt = int(ump_deg.cprs.lrs.argmax().split('_')[0])\n",
    "print(cpt)\n",
    "ump_deg.show_parse_rt(ump_deg.rts[cpt])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>buy_deg_ang42</th>\n",
       "      <th>buy_deg_ang252</th>\n",
       "      <th>buy_deg_ang60</th>\n",
       "      <th>buy_deg_ang21</th>\n",
       "      <th>ind</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-10-22</th>\n",
       "      <td>0</td>\n",
       "      <td>112.561</td>\n",
       "      <td>66.153</td>\n",
       "      <td>36.340</td>\n",
       "      <td>2.480</td>\n",
       "      <td>350</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-18</th>\n",
       "      <td>0</td>\n",
       "      <td>41.653</td>\n",
       "      <td>17.688</td>\n",
       "      <td>43.106</td>\n",
       "      <td>32.407</td>\n",
       "      <td>4784</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-17</th>\n",
       "      <td>1</td>\n",
       "      <td>52.224</td>\n",
       "      <td>17.227</td>\n",
       "      <td>47.510</td>\n",
       "      <td>17.083</td>\n",
       "      <td>11685</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-08</th>\n",
       "      <td>0</td>\n",
       "      <td>43.215</td>\n",
       "      <td>19.480</td>\n",
       "      <td>37.382</td>\n",
       "      <td>22.330</td>\n",
       "      <td>11960</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-08</th>\n",
       "      <td>0</td>\n",
       "      <td>43.215</td>\n",
       "      <td>19.480</td>\n",
       "      <td>37.382</td>\n",
       "      <td>22.330</td>\n",
       "      <td>11961</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-29</th>\n",
       "      <td>0</td>\n",
       "      <td>46.837</td>\n",
       "      <td>18.532</td>\n",
       "      <td>42.129</td>\n",
       "      <td>18.050</td>\n",
       "      <td>21051</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-28</th>\n",
       "      <td>0</td>\n",
       "      <td>46.070</td>\n",
       "      <td>17.231</td>\n",
       "      <td>43.135</td>\n",
       "      <td>8.398</td>\n",
       "      <td>24659</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-31</th>\n",
       "      <td>1</td>\n",
       "      <td>43.903</td>\n",
       "      <td>8.968</td>\n",
       "      <td>39.502</td>\n",
       "      <td>14.803</td>\n",
       "      <td>24825</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-14</th>\n",
       "      <td>1</td>\n",
       "      <td>51.156</td>\n",
       "      <td>14.066</td>\n",
       "      <td>44.081</td>\n",
       "      <td>30.921</td>\n",
       "      <td>25345</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01</th>\n",
       "      <td>0</td>\n",
       "      <td>45.517</td>\n",
       "      <td>21.550</td>\n",
       "      <td>52.711</td>\n",
       "      <td>1.556</td>\n",
       "      <td>26087</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-12</th>\n",
       "      <td>0</td>\n",
       "      <td>45.523</td>\n",
       "      <td>15.090</td>\n",
       "      <td>43.068</td>\n",
       "      <td>12.101</td>\n",
       "      <td>35343</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-12</th>\n",
       "      <td>0</td>\n",
       "      <td>27.139</td>\n",
       "      <td>15.802</td>\n",
       "      <td>35.838</td>\n",
       "      <td>27.009</td>\n",
       "      <td>35344</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-13</th>\n",
       "      <td>0</td>\n",
       "      <td>48.698</td>\n",
       "      <td>17.392</td>\n",
       "      <td>47.938</td>\n",
       "      <td>10.228</td>\n",
       "      <td>35358</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-13</th>\n",
       "      <td>0</td>\n",
       "      <td>48.698</td>\n",
       "      <td>17.392</td>\n",
       "      <td>47.938</td>\n",
       "      <td>10.228</td>\n",
       "      <td>35359</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-13</th>\n",
       "      <td>0</td>\n",
       "      <td>49.137</td>\n",
       "      <td>3.866</td>\n",
       "      <td>45.093</td>\n",
       "      <td>12.464</td>\n",
       "      <td>38016</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-17</th>\n",
       "      <td>0</td>\n",
       "      <td>53.635</td>\n",
       "      <td>15.036</td>\n",
       "      <td>46.648</td>\n",
       "      <td>32.124</td>\n",
       "      <td>39757</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-17</th>\n",
       "      <td>0</td>\n",
       "      <td>43.903</td>\n",
       "      <td>18.308</td>\n",
       "      <td>47.143</td>\n",
       "      <td>7.533</td>\n",
       "      <td>39762</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-17</th>\n",
       "      <td>0</td>\n",
       "      <td>50.732</td>\n",
       "      <td>15.524</td>\n",
       "      <td>46.066</td>\n",
       "      <td>16.162</td>\n",
       "      <td>39769</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-17</th>\n",
       "      <td>0</td>\n",
       "      <td>55.602</td>\n",
       "      <td>17.370</td>\n",
       "      <td>51.020</td>\n",
       "      <td>26.704</td>\n",
       "      <td>39770</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-19</th>\n",
       "      <td>0</td>\n",
       "      <td>47.295</td>\n",
       "      <td>14.163</td>\n",
       "      <td>42.484</td>\n",
       "      <td>16.865</td>\n",
       "      <td>39784</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            result  buy_deg_ang42  buy_deg_ang252  buy_deg_ang60  \\\n",
       "2012-10-22       0        112.561          66.153         36.340   \n",
       "2013-02-18       0         41.653          17.688         43.106   \n",
       "2013-09-17       1         52.224          17.227         47.510   \n",
       "2013-10-08       0         43.215          19.480         37.382   \n",
       "2013-10-08       0         43.215          19.480         37.382   \n",
       "2014-07-29       0         46.837          18.532         42.129   \n",
       "2014-10-28       0         46.070          17.231         43.135   \n",
       "2014-10-31       1         43.903           8.968         39.502   \n",
       "2014-11-14       1         51.156          14.066         44.081   \n",
       "2014-12-01       0         45.517          21.550         52.711   \n",
       "...            ...            ...             ...            ...   \n",
       "2015-08-12       0         45.523          15.090         43.068   \n",
       "2015-08-12       0         27.139          15.802         35.838   \n",
       "2015-08-13       0         48.698          17.392         47.938   \n",
       "2015-08-13       0         48.698          17.392         47.938   \n",
       "2015-11-13       0         49.137           3.866         45.093   \n",
       "2016-02-17       0         53.635          15.036         46.648   \n",
       "2016-02-17       0         43.903          18.308         47.143   \n",
       "2016-02-17       0         50.732          15.524         46.066   \n",
       "2016-02-17       0         55.602          17.370         51.020   \n",
       "2016-02-19       0         47.295          14.163         42.484   \n",
       "\n",
       "            buy_deg_ang21    ind  cluster  \n",
       "2012-10-22          2.480    350       47  \n",
       "2013-02-18         32.407   4784       47  \n",
       "2013-09-17         17.083  11685       47  \n",
       "2013-10-08         22.330  11960       47  \n",
       "2013-10-08         22.330  11961       47  \n",
       "2014-07-29         18.050  21051       47  \n",
       "2014-10-28          8.398  24659       47  \n",
       "2014-10-31         14.803  24825       47  \n",
       "2014-11-14         30.921  25345       47  \n",
       "2014-12-01          1.556  26087       47  \n",
       "...                   ...    ...      ...  \n",
       "2015-08-12         12.101  35343       47  \n",
       "2015-08-12         27.009  35344       47  \n",
       "2015-08-13         10.228  35358       47  \n",
       "2015-08-13         10.228  35359       47  \n",
       "2015-11-13         12.464  38016       47  \n",
       "2016-02-17         32.124  39757       47  \n",
       "2016-02-17          7.533  39762       47  \n",
       "2016-02-17         16.162  39769       47  \n",
       "2016-02-17         26.704  39770       47  \n",
       "2016-02-19         16.865  39784       47  \n",
       "\n",
       "[67 rows x 7 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_failed_cluster_orders = ump_deg.nts[ump_deg.cprs.lrs.argmax()]\n",
    "# 表11-3所示\n",
    "max_failed_cluster_orders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于不是同一份沙盒数据，所以下面结果内容与书中分析内容不符，需要按照实际情况分析:\n",
    "\n",
    "比如下面的特征即是42日和60日的deg格外大，21和252相对训练集平均值也很大："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类簇中deg_ang60平均值为45.12\n",
      "分类簇中deg_ang21平均值为16.93\n",
      "分类簇中deg_ang42平均值为44.49\n",
      "分类簇中deg_ang252平均值为20.62\n"
     ]
    },
    {
     "data": {
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Gb6LTw6rqmCRvS3JSkm8k+Znu/tQU6wKASf1cktdV1deTfD7Js2dcDwCbNOk1LU9MstDd\nj6yqxyR5ZZIfnV5ZALBx3f25JKeOpz+e5LSZFgTAVE16ethNSRaq6h5J7p3k69MrCQAA4A6TjrR8\nKaNTwz6V5L5JnnS4hY/GbSTdGo6ttt3eY9ttfzZLewDAcE0aWv5tkmu7+yVVdWKS66rqwd1962oL\nb/VtJHf6reE4OrbTe8xn5s52UnsIZwDMo0lDy77ccUrYF5Ick8QvcgEAAFM3aWh5dZLLq+ojSY5N\ncn53f3l6ZQEAAIxMFFq6+0tJfnzKtQAAANyNH5cEAAAGTWgBAAAGTWgBAAAGTWgBAAAGTWgBAAAG\nTWgBAAAGTWgBAAAGTWgBAAAGTWgBAAAGTWgBAAAGTWgBAAAGTWgBAAAGbWHWBWzUWRddN9PtX37e\no2e6fWbPexAAYDaMtAAAAIMmtAAAAIMmtAAAAIMmtAAAAIMmtAAAAIMmtAAAAIMmtAAAAIMmtAAA\nAIMmtAAAAIMmtAAAAIMmtAAAAIMmtAAAAIMmtAAAAIO2MOkLq+olSZ6c5Ngkb+jut06tKgAAgLGJ\nRlqqam+SRyY5LckZSU6cYk0AAAB/Z9KRlscl+ZMkVye5d5IXH27hPXtOyMLC7gk3NQxnXXTdrEtg\nh1taWhz0+uad9gCA4Zo0tNw3yQOTPCnJtyX5rar6x919cLWF9+07MOFmgEOWl/dPbV1LS4tTXd+8\n20ntIZwBMI8mDS03J/lUd38tSVfVrUmWkvzN1CoDAADI5HcP+2iSx1fVrqp6QJJ7ZhRkAAAApmqi\n0NLd70tyQ5KPJXlvkud29zemWRgAAECyiVsed/c50ywEACZVVackeVV3762qByW5IsnBJDdmdGDt\n9lnWB8Dm+HFJAOZaVZ2T5LIkx49nXZrkgu4+PcmuJGfOqjYApmPikRYAGIjPJHlKkneMH5+c5EPj\n6WuSPDajW/Qf1mZvz/9DL/zNiV/LdBztu+O5G9/Gaasjo73uTmgBYK5191VVddKKWbtW3IJ/f5L7\nbGQ9bs8//47mrct30q3SN0tbHZmd3l5rBTanhwGw3ay8fmUxyS2zKgSA6RBaANhubqiqvePpJyT5\nyAxrAWAKnB4GwHbzwiRvqapjk3wyyZUzrgeATRJaAJh73f25JKeOp29KcsZMCwJgqpweBgAADJrQ\nAgAADJrQAgAADJrQAgAADJoL8WFOnHXRdbMuIZef9+hZlwAA7EBGWgAAgEETWgAAgEETWgAAgEET\nWgAAgEETWgAAgEETWgAAgEETWgAAgEETWgAAgEETWgAAgEETWgAAgEETWgAAgEETWgAAgEETWgAA\ngEETWgAAgEFb2MyLq+p+Sa5P8pju/tR0SgIAALjDxCMtVXVMkjcn+cr0ygEAALizzYy0XJzkTUle\nst6Ce/ackIWF3ZvYFDAES0uLsy5hy2znfQOAeTdRaKmqZyVZ7u5rq2rd0LJv34FJNgMMzPLy/lmX\nsCWWlha37b7dlXAGwDya9PSws5I8pqo+mOShSd5eVX9/alUBAACMTTTS0t2POjQ9Di4/292fn1ZR\nAAAAh7jlMQAAMGibuuVxknT33inUAQAAsCojLQAAwKAJLQAAwKAJLQAAwKAJLQAAwKAJLQAAwKBt\n+u5hAABDcNZF1810+5ef9+iZbh+2MyMtAADAoAktAADAoAktAADAoAktAADAoAktAADAoLl7GLBh\n7swDAMyC0ALAtlRVH0/yxfHDz3b3v5plPQBMTmgBYNupquOT7OruvbOuBYDNE1oA2I4ekuSEqnp/\nRn3d+d39B4d7wZ49J2RhYfdRKY7taWlpcdYlDJa2OTLa6+6EFgC2owNJLk5yWZLvSHJNVVV337bW\nC/btO3C0amObWl7eP+sSBmlpaVHbHIGd3l5rBTahBYDt6KYkf97dB5PcVFU3J/nWJH8x27IAmIRb\nHgOwHZ2V5JIkqaoHJLl3kr+aaUUATMxICwDb0VuTXFFVH01yMMlZhzs1DIBhE1oA2Ha6+2tJfnLW\ndQAwHU4PAwAABk1oAQAABk1oAQAABk1oAQAABk1oAQAABm2iu4dV1TFJLk9yUpLjkryiu39rinUB\nAAAkmXyk5elJbu7u05M8Psnrp1cSAADAHSb9nZZ3J7lyPL0riR/sAgAAtsREoaW7v5QkVbWYUXi5\n4HDL79lzQhYWdk+yKYC/s7S0OJfrBgA2Z9KRllTViUmuTvKG7n7n4Zbdt+/ApJsB+DvLy/u3ZL1L\nS4tbtu6hEc4AmEeTXoh//yTvT/K87v7AdEsCAAC4w6QjLecn2ZPkwqq6cDzvCd39lemUBQAAMDLp\nNS1nJzl7yrUAAADcjR+XBAAABk1oAQAABk1oAQAABk1oAQAABk1oAQAABk1oAQAABk1oAQAABk1o\nAQAABk1oAQAABk1oAQAABk1oAQAABm1h1gUAzIuzLrpuptu//LxHz3T7ADArRloAAIBBE1oAAIBB\nE1oAAIBBE1oAAIBBE1oAAIBBE1oAAIBBE1oAAIBB8zstAACwDcz698SSrftNMSMtAADAoAktAADA\noAktAADAoAktAADAoAktAADAoAktAADAoE10y+OqukeSNyR5SJKvJvnp7v7zaRYGAJPSTwFsL5OO\ntPxwkuO7+xFJzktyyfRKAoBN008BbCO7Dh48eMQvqqpLk3ysu39j/Ph/d/c/mHZxADAJ/RTA9jLp\nSMu9k/ztisffqKqJTjUDgC2gnwLYRiYNLV9MsrhyPd192xTqAYBp0E8BbCOThpbfS/LEJKmqU5P8\nydQqAoDN008BbCOTDpVfneQxVfXfkuxK8q+mVxIAbJp+CmAbmehCfAAAgKPFj0sCAACDJrQAAACD\nNre3f/Rrx3dWVackeVV3762qByW5IsnBJDcmeW533z7L+o6WqjomyeVJTkpyXJJXJPmz7ND2SJKq\n2p3kLUkqozb42SS3Zge3SZJU1f2SXJ/kMUluyw5vD3Ymn4ONq6qXJHlykmMz+vvjQ9FedzPuh9+W\nUT/8jSQ/E++tVW3kb7eq+pkkz8moDV/R3e+bWcEzNs8jLX7teKyqzklyWZLjx7MuTXJBd5+e0QWo\nZ86qthl4epKbx/v++CSvz85ujyT5oSTp7tOSXJDkldnhbTLuVN+c5CvjWTu6PdiZfA42rqr2Jnlk\nktOSnJHkxGivtTwxyUJ3PzLJy6PPWdVG/narqr+f5N9k9L57XJJfrKrjZlHvEMxzaPm+JL+TJN39\nB0m+d7blzNRnkjxlxeOTMzoClCTXJPmBo17R7Lw7yYXj6V0ZHZnYye2R7v7PSZ49fvjAJLdkh7dJ\nkouTvCnJX44f7/T2YGfyOdi4x2V02+yrk7w3yfuivdZyU5KF8Rkx907y9Wir1Wzkb7eHJ/m97v5q\nd/9tkj9P8j1HtcoBmefQ4teOx7r7qoy+FA7Z1d2Hbgu3P8l9jn5Vs9HdX+ru/VW1mOTKjEYWdmx7\nHNLdt1XV25K8LsmvZwe3SVU9K8lyd1+7YvaObQ92Jp+DI3bfjA6O/lhGp9j+ekY/WKq97u5LGZ0a\n9qmMTk1+bby37maDf7vd9W/dHd128xxa/Nrx2laeJ7qY0ZH1HaOqTkzyu0ne0d3vzA5vj0O6+6eS\nfGdGncjfW/HUTmuTszL6/Y4PJnlokrcnud+K53dae7Az+RwcmZuTXNvdX+vuzui6wJV/PGqvO/zb\njNrqOzO67vhtGV0HdIi2Wt1qf6vc9W/dHd128xxa/Nrx2m4Yn3+bJE9I8pEZ1nJUVdX9k7w/ybnd\nffl49o5tjySpqmeMLyBNkgMZfTH+0U5tk+5+VHef0d17k3wiyTOTXLNT24OdyefgiH00yeOraldV\nPSDJPZN8QHutal/uGB34QpJjssP74Q1arY0+luT0qjq+qu6T5J9kdJH+jjTPp1P5teO1vTDJW6rq\n2CSfzOg0qZ3i/CR7klxYVYeubTk7yWt3aHskyXuS/GpVfTijzuMFGbXDTn2PrGYnf2bgEJ+DNXT3\n+6rqURn9EXmPJM9N8tlor9W8OsnlVfWRjEZYzk/yR9FW67nb56+7v1FVr80owNwjyUu7+9ZZFjlL\nuw4ePLj+UgAAADMyz6eHAQAAO4DQAgAADJrQAgAADJrQAgAADJrQAgAADJrQAgAADJrQAgAADJrQ\nAgAADJrQAgAADJrQAgAADJrQAgAADJrQAgAADJrQAgAADJrQAgAADJrQAgAADJrQAgAADJrQAgAA\nDJrQAgAADJrQAgAADJrQAgAADJrQAgAADNrCrAuAqtqb5PXd/d1HcZtPTfK87t57tLY5qaraneTC\nJE9Ocs8k/yXJz3f3wao6JcmvjOf/ZZKnd/dfzaxYgG1C33R4VbWU5M1JHpTR35O/neTc7r69qp6f\n5KVJPj9efH93n15V90hyUZIfTHJ7kk8neU53Lx/1HWDuGGmB4Ts7yd4kpyX5niSPSPK0qjo2yZVJ\nzu7ufzKefuusigRgR3l1kj/r7u9J8s+SnJLkWePnHpnRwbWHjv87fTz/rCQnJ/ln3f3gJH+e5JKj\nWzbzykgLQ3GvqroyoyM2tyR5dpLzk9zY3RcnSVVdkeTGJL+f5DeSPHB8ROeEJJ9L8t3d/TdrbaCq\nXp7kXya5OaOjO4fmH5vkVUnOSLI7yQ1J/k13f7GqHp7kDUmOTfKZJA/M6Iv4g4fZzndmNPpxryQP\nSPKJJE/r7lur6taMjjI9Zvzca7r7l8ejKb+U0WjK3yb5wyTfNT7a9swkL+rur4zX/6NJvpbk/07y\nxe7+vfGm35rkl6vqW7r75rXqA2DD9E1r901XJ/m9JBmv48ZxHckotCxW1YuT/E1GfdifJPnTJC/u\n7q+Ol/ujJM9dq2ZYyUgLQ3Fikku7+6FJ3pnkHWstOP4j/eYkjx/P+okkH1inUzgzyY8meWhGX6b3\nWfH0eUluS3Jydz8ko9OsLqqqhSRXJblwfCTptePXr+dnkrytux+RUUf3bRkNhSfJcUn+T3efluSp\n4+0cn+SnMzr69N0ZjaT8oxXr+84k31VVH6iqP07yc0m+kFGb/cWKdvlakuUk/2ADNQKwPn3TGn1T\nd1/V3Z8f78fDkvxkkqur6p5JPpXkP3T3wzI6oHZNVd2ru3+/uz8+fs2eJP8uybs3UDsILQzGH3f3\nfxtPX5Hke3PnL++7+pWMvoCT5DlJ3rjO+n8gyXu6e39335bk8hXPPSnJmUluqKpPJPnhJN+V5MFJ\n0t3XjP//uxkdTVvPuUmWq+qccV0PyOjI1iG/Of7/xzPqKO6Z5IlJ3t7dt47Dx5tXLH9MklPHy5yW\n5PuSPD9rf36/sYEaAVifvmntvilJUlWPS/L+JM/v7k9095e7+3GH2q27/1OSfRmdHXDoNf8oyYeT\nfDSjNoN1CS0MxV3/0D6Y0VD8rhXzjl0x/etJvq+q/nmSe3X3h9dZ/8G7rOu2FdO7M7ou5KHjo2kP\nz+hI0213ec1qda7mP2Z0CsH/zOic34/fZT1fSZLuPjh+vGuVba3czl8m+Y3u/mp378/oqNQjkvyv\nJN96aKGqOibJfZP87w3UCMD69E1r902pqp/PaPTpX3T3O8bzHji+EH+lXUm+Pn7+n2d0Kt3buvtn\nV2wPDktoYSgeUlWHhrefk9HRl+WMjmqlqu6b5NCFfOnuA0l+LaOjUm/awPp/J8mPVdU3je9e8owV\nz12b5HlVdez4ubck+cUkn0zy1ap6/LiGh2d0hGu9L9jHJXl5d79rvOwpGXU+h/PbSZ5eVceNh/6f\ntWI7V46fu8c4mDwpyf+X0bnF31JVjxwvd1aS3+/uW9bZFgAbo29ao28aB5bnJjm1u//ritd8Ockr\nxnWlqp6Y5IQkHxv3V1cneeaha4Jgo1yIz1B8MsnLqurbM7po76cyuh3ir1dVZ3Qx4wfv8ppfzeio\n0dvXW3l3/5eqenBGF/3tS/LfkyyNn/6FJBdndJHj7owuTnxhd982vuj9TVX1i0luyuj2jQfW2dz5\nGZ3X+4Xxsh/K6Pzhw7kiSY1r+FKSz67YzgUZXYx5Y0af2f83yS+P63tKktePzyG+OaOL9gGYDn3T\nKn3T+CYBv5DRqNN7qurQ8u/u7ldW1Y8nefN4uS8m+ZHu/lpV/fuMRl0uqqqLxq/5bHf/yDp1QHYd\nPGhUjvmKSE6bAAAXw0lEQVRTVbsyOj/3gd39c1u4nV9KcnF3/3VVnZhRh/Lt0x7NqKrHJrlfd//a\n+PFrktza3edOczsAbB19E2wdIy3Mq/+R0RD9kw/NqKp3ZXREaDVP6+6eYDv/M8kHqurrGR0d+ukk\n96+qD66xfHf30ybYzp8mefH49pALGXVAW9bhAbAl9E2wRYy0AAAAg+ZCfAAAYNCEFgAAYNCOyjUt\ny8v773YO2p49J2TfvvVudLHzaJfVaZfVaZfVaZfV7dlzQhYWdt/19x0YW62vmhfb7T1vf4bN/gzb\nvO/P0tLiqv3UzEZaFhbWuzX4zqRdVqddVqddVqddVqddtq/t9m9rf4bN/gzbdtufQ5weBgAADJpb\nHgMw96rqfkmuT/KYJLdl9KN4BzP6Udbndvfts6sOgM0y0gLAXKuqY5K8OclXxrMuTXJBd5+e0W9Y\nnDmr2gCYDiMtAMy7i5O8KclLxo9PTvKh8fQ1SR6b5Or1VjK+UcGWFHg0LC0tzrqEqbI/w2Z/hm27\n7U8itAAwx6rqWUmWu/vaqjoUWnZ196E7ge1Pcp+NrGvO77aT5eX9sy5jauzPsNmfYZv3/VkrcAkt\nAMyzs5IcrKofSPLQJG9Pcr8Vzy8muWUWhQEwPa5pAWBudfejuvuM7t6b5BNJnpnkmqraO17kCUk+\nMqPyAJgSIy0AbDcvTPKWqjo2ySeTXDnjegDYJKEFgG1hPNpyyBmzqgOA6RNa5sRZF1030+1fft6j\nZ7r9RBsADNmsv6MT39OwnbmmBQAAGDShBQAAGDShBQAAGDShBQAAGDShBQAAGDShBQAAGDShBQAA\nGDShBQAAGDShBQAAGDShBQAAGDShBQAAGDShBQAAGDShBQAAGDShBQAAGDShBQAAGLSFjSxUVack\neVV3762qhyZ5XZJvJPlqkmd2919vYY0AAMAOtu5IS1Wdk+SyJMePZ70myfO7e2+S9yQ5d8uqAwAA\ndryNnB72mSRPWfH4J7r7E+PphSS3Tr0qAACAsXVPD+vuq6rqpBWP/ypJquqRSZ6X5FHrrWPPnhOy\nsLD7bvOXlhaPpNYdY4jtMsSajrahtsFQ65o17QIA28eGrmm5q6p6WpKXJvnB7l5eb/l9+w7cbd7S\n0mKWl/dPsvltbajtMuuahvAH6KzbYDVDfb/MmnZZ3RA+RwAwiSMOLVX19CTPSbK3u78w/ZIAAADu\ncEShpap2J3ltkv+V5D1VlSQf6u6XbUFtALCucd/0liSV5GCSn01yTJL3Jfn0eLE3dve7ZlMhAJu1\nodDS3Z9Lcur44TdvWTUAcOR+KEm6+7Sq2pvklUnem+TS7r5kloUBMB1+XBKAudbd/znJs8cPH5jk\nliQnJ/nBqvpwVb21qlzQAzDHJroQHwCGpLtvq6q3JfmRJE9N8g+SXNbd11fVS5O8LMmLDreOte50\nOS/caGHYbTDk2iZhf4Ztu+1PIrQAsE10909V1blJ/jDJI7v7f4+fujrJ69Z7/Wp3upwX7pg3MtQ2\n2G7/PvZn2OZ9f9YKXE4PA2CuVdUzquol44cHktye0c1iHj6e9/1Jrp9JcQBMhZEWAObde5L8alV9\nOKO7hr0gyV8keV1VfT3J53PHNS8AzCGhBYC51t1fTvLjqzx12tGuBYCt4fQwAABg0IQWAABg0IQW\nAABg0IQWAABg0IQWAABg0IQWAABg0IQWAABg0PxOCxty1kXXzbqEmZt1G1x+3qNnun0AgFkx0gIA\nAAya0AIAAAya0AIAAAya0AIAAAya0AIAAAya0AIAAAya0AIAAAya0AIAAAya0AIAAAya0AIAAAya\n0AIAAAya0AIAAAya0AIAAAya0AIAAAzawkYWqqpTkryqu/dW1YOSXJHkYJIbkzy3u2/fuhIBYG1V\ntTvJW5JURn3Tzya5NfoqgG1j3ZGWqjonyWVJjh/PujTJBd19epJdSc7cuvIAYF0/lCTdfVqSC5K8\nMvoqgG1lI6eHfSbJU1Y8PjnJh8bT1yT5gWkXBQAb1d3/Ocmzxw8fmOSW6KsAtpV1Tw/r7quq6qQV\ns3Z198Hx9P4k91lvHXv2nJCFhd13m7+0tLjBMncW7cJq1npfeL+sTrvsLN19W1W9LcmPJHlqksdM\nq6+aF97zw26DIdc2CfszbNttf5INXtNyFyvPCV7M6IjWYe3bd+Bu85aWFrO8vH+CzW9v2oW1rPa+\n8H5ZnXZZ3XbsxFbq7p+qqnOT/GGSv7fiqYn7qnnhPT8y1DbYbv8+9mfY5n1/1uqrJrl72A1VtXc8\n/YQkH5mwJgDYtKp6RlW9ZPzwQEYH1/5IXwWwfUwy0vLCJG+pqmOTfDLJldMtCQCOyHuS/GpVfTjJ\nMUlekFH/pK8C2CY2FFq6+3NJTh1P35TkjC2sCQA2rLu/nOTHV3lKXwWwTfhxSQAAYNCEFgAAYNCE\nFgAAYNCEFgAAYNCEFgAAYNCEFgAAYNCEFgAAYNCEFgAAYNCEFgAAYNCEFgAAYNCEFgAAYNCEFgAA\nYNCEFgAAYNCEFgAAYNCEFgAAYNCEFgAAYNCEFgAAYNCEFgAAYNCEFgAAYNCEFgAAYNCEFgAAYNCE\nFgAAYNAWZl0AAGxGVR2T5PIkJyU5LskrkvxFkvcl+fR4sTd297tmUiAAmya0ADDvnp7k5u5+RlV9\nc5JPJHl5kku7+5LZlgbANAgtAMy7dye5cjy9K8ltSU5OUlV1ZkajLS/o7v0zqg+ATRJaAJhr3f2l\nJKmqxYzCywUZnSZ2WXdfX1UvTfKyJC863Hr27DkhCwu7t7rcLbO0tDjrEmZuyG0w5NomYX+Gbbvt\nTyK0ALANVNWJSa5O8obufmdVfVN33zJ++uokr1tvHfv2HdjKErfU0tJilpcNJA21Dbbbv4/9GbZ5\n35+1Ape7hwEw16rq/knen+Tc7r58PPvaqnr4ePr7k1w/k+IAmIqJRlrGd2p5W0Z3avlGkp/p7k9N\nsS4A2Kjzk+xJcmFVXTie9/NJXl1VX0/y+STPnlVxAGzepKeHPTHJQnc/sqoek+SVSX50emUBwMZ0\n99lJzl7lqdOOdi0AbI1JTw+7KclCVd0jyb2TfH16JQEAANxh0pGWL2V0atinktw3yZMOt/Bad2TZ\njnc2mAbtwmrWel94v6xOuwDA9jFpaPm3Sa7t7peM79hyXVU9uLtvXW3h1e7IMu93Ntgq2oW1rPa+\n8H5ZnXZZnSAHwLyaNLTsyx2nhH0hyTFJ5vfm9gAAwGBNGlpeneTyqvpIkmOTnN/dX55eWQAAACMT\nhZbxrw//+JRrAQAAuBs/LgkAAAya0AIAAAya0AIAAAya0AIAAAya0AIAAAya0AIAAAya0AIAAAya\n0AIAAAya0AIAAAya0AIAAAya0AIAAAya0AIAAAya0AIAAAzawqwL2KizLrpuptu//LxHz3T7AACw\nUxlpAQAABk1oAQAABk1oAQAABm1urmkBgNVU1TFJLk9yUpLjkrwiyZ8luSLJwSQ3Jnlud98+oxIB\n2CQjLQDMu6cnubm7T0/y+CSvT3JpkgvG83YlOXOG9QGwSUZaAJh3705y5Xh6V5Lbkpyc5EPjedck\neWySqw+3kj17TsjCwu6tqnHLLS0tzrqEmRtyGwy5tknYn2HbbvuTCC0AzLnu/lKSVNViRuHlgiQX\nd/fB8SL7k9xnvfXs23dgy2rcaktLi1le3j/rMmZuqG2w3f597M+wzfv+rBW4nB4GwNyrqhOT/G6S\nd3T3O5OsvH5lMcktMykMgKkQWgCYa1V1/yTvT3Jud18+nn1DVe0dTz8hyUdmURsA0+H0MADm3flJ\n9iS5sKouHM87O8lrq+rYJJ/MHde8ADCHhBYA5lp3n51RSLmrM452LQBsDaeHAQAAgya0AAAAgya0\nAAAAgya0AAAAgzbxhfhV9ZIkT05ybJI3dPdbp1YVAADA2EQjLeN73z8yyWkZ3Z3lxCnWBAAA8Hcm\nHWl5XJI/SXJ1knsnefHhFt6z54QsLOy+2/ylpcUJN3/0Hc1a56ldOHrWel94v6xOuwDA9jFpaLlv\nkgcmeVKSb0vyW1X1j7v74GoL79t34G7zlpYWs7y8f8LNH31Hq9Z5axeOntXeF94vq9MuqxPkAJhX\nk4aWm5N8qru/lqSr6tYkS0n+ZmqVAQAAZPK7h300yeOraldVPSDJPTMKMgAAAFM1UWjp7vcluSHJ\nx5K8N8lzu/sb0ywMAAAg2cQtj7v7nGkWAgAAsBo/LgkAAAya0AIAAAya0AIAAAya0AIAAAya0AIA\nAAya0AIAAAya0AIAAAya0AIAAAya0AIAAAya0AIAAAzawqwLAIDNqqpTkryqu/dW1cOSvC/Jp8dP\nv7G73zW76gDYLKEFgLlWVeckeUaSL49nnZzk0u6+ZHZVATBNQgsA8+4zSZ6S5B3jxycnqao6M6PR\nlhd09/71VrJnzwlZWNi9dVVusaWlxVmXMHNDboMh1zYJ+zNs221/EqEFgDnX3VdV1UkrZn0syWXd\nfX1VvTTJy5K8aL317Nt3YIsq3HpLS4tZXl43l217Q22D7fbvY3+Gbd73Z63A5UJ8ALabq7v7+kPT\nSR42y2IA2DyhBYDt5tqqevh4+vuTXH+4hQEYPqeHAbDd/FyS11XV15N8PsmzZ1wPAJsktAAw97r7\nc0lOHU9/PMlpMy0IgKlyehgAADBoQgsAADBoQgsAADBoQgsAADBoQgsAADBoQgsAADBoQgsAADBo\nQgsAADBoQgsAADBoQgsAADBoC5t5cVXdL8n1SR7T3Z+aTkkAAAB3mHikpaqOSfLmJF+ZXjkAAAB3\ntpmRlouTvCnJS9ZbcM+eE7KwsPtu85eWFjex+aPraNY6T+3C0XPWRdfNuoSZe+8lZ254WZ8jdhrf\nEcB2NlFoqapnJVnu7murat3Qsm/fgbvNW1pazPLy/kk2PxNHq9Z5axc4mjb62fA5Wp0gB8C8mvT0\nsLOSPKaqPpjkoUneXlV/f2pVAQAAjE000tLdjzo0PQ4uP9vdn59WUQAAAIe45TEAADBom7rlcZJ0\n994p1AEAALAqIy0AAMCgCS0AAMCgCS0AAMCgCS0AAMCgbfpCfACYtao6JcmruntvVT0oyRVJDia5\nMclzu/v2WdYHwOYYaQFgrlXVOUkuS3L8eNalSS7o7tOT7Epy5qxqA2A6jLQAMO8+k+QpSd4xfnxy\nkg+Np69J8tgkV6+3kj17TsjCwu4tKZCjY2lpcdYlrGnItU3C/gzbdtufRGgBYM5191VVddKKWbu6\n++B4en+S+2xkPfv2HZh2aUfNdvwDZRLLy/tnXcKqlpYWB1vbJOzPsM37/qz1feb0MAC2m5XXrywm\nuWVWhQAwHUILANvNDVW1dzz9hCQfmWEtAEyB08MA2G5emOQtVXVskk8muXLG9QCwSUILAHOvuz+X\n5NTx9E1JzphpQQBMldPDAACAQRNaAACAQRNaAACAQRNaAACAQRNaAACAQRNaAACAQRNaAACAQfM7\nLQDAtnDWRdfNdPuXn/fomW4ftjMjLQAAwKAJLQAAwKAJLQAAwKAJLQAAwKAJLQAAwKAJLQAAwKAJ\nLQAAwKAJLQAAwKBN9OOSVXVMksuTnJTkuCSv6O7fmmJdAAAASSYfaXl6kpu7+/Qkj0/y+umVBAAA\ncIeJRlqSvDvJlePpXUlum045AAAAdzZRaOnuLyVJVS1mFF4uONzye/ackIWF3Xebv7S0OMnmZ+Ks\ni66bdQmw4x3Jd8Y8fb8AAIc36UhLqurEJFcneUN3v/Nwy+7bd+Bu85aWFrO8vH/SzQM70Ea/M3y/\nrE6QA2BeTXoh/v2TvD/J87r7A9MtCQAA4A6TjrScn2RPkgur6sLxvCd091emUxYAbE5VfTzJF8cP\nP9vd/2qW9QAwuUmvaTk7ydlTrgUApqKqjk+yq7v3zroWADZv4mtaAGDAHpLkhKp6f0Z93fnd/Qcz\nrgmACQktAGxHB5JcnOSyJN+R5Jqqqu5e8xb9a93pEjbqcDe72G43wrA/w7bd9icRWgDYnm5K8ufd\nfTDJTVV1c5JvTfIXa71gtTtdzovt+AfKPFrrroXb7Y6G9mfY5n1/1vo+u8dRrgMAjoazklySJFX1\ngCT3TvJXM60IgIkZaQFgO3prkiuq6qNJDiY563CnhgEwbEILANtOd38tyU/Oug4ApsPpYQAAwKAZ\naQEAmIKzLrpuptu//LxHz3T7sJWMtAAAAIMmtAAAAIMmtAAAAIMmtAAAAIMmtAAAAIPm7mHA3Jj1\nnXlmzZ2BANipjLQAAACDJrQAAACDJrQAAACDJrQAAACDJrQAAACD5u5hADAFO/3udgBbyUgLAAAw\naEILAAAwaEILAAAwaEILAAAwaEILAAAwaO4eBgCwDcz6DnaXn/fomW6f2b8Hkq17HxhpAQAABk1o\nAQAABm2i08Oq6h5J3pDkIUm+muSnu/vPp1kYAExKPwWwvUw60vLDSY7v7kckOS/JJdMrCQA2TT8F\nsI1MGlq+L8nvJEl3/0GS751aRQCwefopgG1k0ruH3TvJ3654/I2qWuju21ZbeGlpcdca8ze8wfde\ncuYRFQjAjnZE/VSydl+1UfopmF9H8jfpkG3n76FJR1q+mGTlv+49DtcRAMBRpp8C+P/bu5dQq6o4\njuPfY1zToiIIhUBqEP2GFUZF0vVCiVbUIGjWyzuIwkFBVBRKkxoE5UCjB6bZwyAqr5Fw80IvswaR\nJhTJ36gGQQR1ITBKUjwN1r64sX0NH5yz91q/D1y4Z+8zWOu//6zH2WvvlZGTnbR8DtwEIOka4JvT\nViIzM7NT537KzCwjJ7s8bAJYJukLoAesPH1FMjMzO2Xup8zMMtLr9/vDLoOZmZmZmdmsvLmkmZmZ\nmZm1mictZmZmZmbWap60mJmZmZlZq53sg/gnRNIZwAZAQB+4DxgBtgPfV197ISLeGkR52kTSAmA3\nsAw4DGwmxehbYFVEHBle6YbnmLjMx7kCgKQ9pFe5AvwEPIVzpiku63DOIOkx4FZgLvA88CnOl2xI\nuhp4OiLGJF1CR6+tpBFgE3AxcCbwJPAd3a1P05jnIB2tD+Q3VsmpLy2pnR/UnZZbACJiCbCalByL\ngbURMVb9lTigGAFeAv6uDq0FVkfEdaS33eS7Q9BxNMSl+FwBkDQP6NXisBLnzGxxKT5nJI0B1wJL\ngKXAIpwv2ZD0CPAyMK861OVrewcwXZV9BfAc3a5P05ins/XJbaySU19aWjs/kElLRGwD7q0+XgT8\nQRpU3Cxpp6SNkvLYivTEPAO8CPxSfV5MmiEDTAI3DKNQLdAUl9JzBeAy4CxJU5I+qvaecM7MHpfS\nc2Y5aW+SCeB90p0n50s+fgBuq33u8rV9G1hT/d8j/ZLf2focZ8zTyfqQ31glp760qHZ+YM+0RMRh\nSa8C64EtwJfAwxExCvwIPDGosrSBpHuA3yJiR+1wLyJm3kF9ADhv4AUbslniUnSu1PxF6jyWk5Yb\nbME5A81x2YNz5gLgSuB2jsZljvMlDxHxLnCodqizbUFE/BkRB6ofF94h3Z3obH2gcczTyfpkOlbJ\nqS8tqp0f6IP4EXE3cClpredUROyuTk0AVwyyLC0wTtr47BPgcuA1YEHt/DmkX2dK0xSXycJzZcZ+\n4I2I6EfEfmAaWFg7X2rONMXlA+cM08COiPgnIoK0pr7eeZWaL7mqr1nv3LWVtAj4GHg9It6k4/WB\n/4x55tdOdak+OY5VcupLi2rnBzJpkXRn9aAQpBnuEWCrpKuqY9eTHvAqRkSMRsTSiBgD9gJ3AZPV\n+kSAG4HPhlS8oZklLu+VnCs148CzAJIuBM4FpkrPGZrjss05wy5ghaReFZezgQ+dL9n6uqvXVtJC\nYAp4NCI2VYe7XJ+mMc9XXaxPpmOVnPrSotr5gbw9DNgKvCJpJ+mtYQ8CPwPrJR0CfuXo+s+SPQRs\nkDQX2Ee6TW5wP84VgI3AZkm7SG8FGQd+xznTFJeDFJ4zEbFd0ihpeeUcYBXpLTml50uuutx/PA6c\nD6yRNPNsywPAuo7Wp2nMs4/uXp9jdTnXIKO+tLR2vtfv9///W2ZmZmZmZkPizSXNzMzMzKzVPGkx\nMzMzM7NW86TFzMzMzMxazZMWMzMzMzNrNU9azMzMzMys1TxpMTMzMzOzVvOkxczMzMzMWu1fUmSj\n2y9V5O0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13fed7748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from abupy import ml\n",
    "\n",
    "ml.show_orders_hist(max_failed_cluster_orders, ['buy_deg_ang21', 'buy_deg_ang42', 'buy_deg_ang60','buy_deg_ang252'])\n",
    "print('分类簇中deg_ang60平均值为{0:.2f}'.format(\n",
    "    max_failed_cluster_orders.buy_deg_ang60.mean()))\n",
    "\n",
    "print('分类簇中deg_ang21平均值为{0:.2f}'.format(\n",
    "    max_failed_cluster_orders.buy_deg_ang21.mean()))\n",
    "\n",
    "print('分类簇中deg_ang42平均值为{0:.2f}'.format(\n",
    "    max_failed_cluster_orders.buy_deg_ang42.mean()))\n",
    "\n",
    "print('分类簇中deg_ang252平均值为{0:.2f}'.format(\n",
    "    max_failed_cluster_orders.buy_deg_ang252.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练数据集中deg_ang60平均值为2.95\n",
      "训练数据集中deg_ang21平均值为5.08\n",
      "训练数据集中deg_ang42平均值为4.94\n",
      "训练数据集中deg_ang252平均值为4.92\n"
     ]
    },
    {
     "data": {
      "image/png": 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37vHXrrG3a9zQ+thNfCRJk8kO4ArgWuD3KRKdjszsL5/vA2YDs4BtNa8bKB/S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\nkiRJUu0ZfCRJkiTV3v8Hpr7U6U4XlHgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x185414f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.show_orders_hist(orders_pd_train, ['buy_deg_ang21', 'buy_deg_ang42', 'buy_deg_ang60', 'buy_deg_ang252'])\n",
    "print('训练数据集中deg_ang60平均值为{0:.2f}'.format(\n",
    "    orders_pd_train.buy_deg_ang60.mean()))\n",
    "\n",
    "print('训练数据集中deg_ang21平均值为{0:.2f}'.format(\n",
    "    orders_pd_train.buy_deg_ang21.mean()))\n",
    "\n",
    "print('训练数据集中deg_ang42平均值为{0:.2f}'.format(\n",
    "    orders_pd_train.buy_deg_ang42.mean()))\n",
    "\n",
    "print('训练数据集中deg_ang252平均值为{0:.2f}'.format(\n",
    "    orders_pd_train.buy_deg_ang252.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plot snap::98.51%"
     ]
    }
   ],
   "source": [
    "progress = AbuProgress(len(max_failed_cluster_orders), 0, label='plot snap')\n",
    "for ind in np.arange(0, len(max_failed_cluster_orders)):\n",
    "    progress.show(ind)\n",
    "    order_ind = int(max_failed_cluster_orders.iloc[ind].ind)\n",
    "    # 交易快照文件保存在~/abu/data/save_png/中\n",
    "    ABuMarketDrawing.plot_candle_from_order(ump_deg.fiter.order_has_ret.iloc[order_ind], save=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "交易快照文件保存在~/abu/data/save_png/中, 下面打开对应目录：save_png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if abupy.env.g_is_mac_os:\n",
    "    !open $abupy.env.g_project_data_dir\n",
    "else:\n",
    "    !echo $abupy.env.g_project_data_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11.2.2 使用全局最优对分类簇集合进行筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AbuUmpMainDeg: brute min progress::100.0%\r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-0.205 , -0.0017,  0.65  ])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brust_min = ump_deg.brust_min()\n",
    "brust_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lcs</th>\n",
       "      <th>lms</th>\n",
       "      <th>lps</th>\n",
       "      <th>lrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54_32</th>\n",
       "      <td>365</td>\n",
       "      <td>-0.0525</td>\n",
       "      <td>-19.1550</td>\n",
       "      <td>0.7096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54_35</th>\n",
       "      <td>629</td>\n",
       "      <td>-0.0248</td>\n",
       "      <td>-15.6015</td>\n",
       "      <td>0.6979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54_38</th>\n",
       "      <td>947</td>\n",
       "      <td>-0.0195</td>\n",
       "      <td>-18.5037</td>\n",
       "      <td>0.6674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54_47</th>\n",
       "      <td>532</td>\n",
       "      <td>-0.0417</td>\n",
       "      <td>-22.1767</td>\n",
       "      <td>0.6579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54_48</th>\n",
       "      <td>663</td>\n",
       "      <td>-0.0184</td>\n",
       "      <td>-12.2099</td>\n",
       "      <td>0.6682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54_53</th>\n",
       "      <td>796</td>\n",
       "      <td>-0.0335</td>\n",
       "      <td>-26.6577</td>\n",
       "      <td>0.7211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55_1</th>\n",
       "      <td>583</td>\n",
       "      <td>-0.0270</td>\n",
       "      <td>-15.7313</td>\n",
       "      <td>0.6998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55_8</th>\n",
       "      <td>1246</td>\n",
       "      <td>-0.0214</td>\n",
       "      <td>-26.6566</td>\n",
       "      <td>0.6790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55_18</th>\n",
       "      <td>429</td>\n",
       "      <td>-0.0209</td>\n",
       "      <td>-8.9820</td>\n",
       "      <td>0.6667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55_23</th>\n",
       "      <td>1054</td>\n",
       "      <td>-0.0168</td>\n",
       "      <td>-17.7548</td>\n",
       "      <td>0.6689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92_57</th>\n",
       "      <td>172</td>\n",
       "      <td>-0.0222</td>\n",
       "      <td>-3.8194</td>\n",
       "      <td>0.6802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92_59</th>\n",
       "      <td>245</td>\n",
       "      <td>-0.0377</td>\n",
       "      <td>-9.2387</td>\n",
       "      <td>0.7265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93_1</th>\n",
       "      <td>546</td>\n",
       "      <td>-0.0239</td>\n",
       "      <td>-13.0463</td>\n",
       "      <td>0.6703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93_8</th>\n",
       "      <td>850</td>\n",
       "      <td>-0.0230</td>\n",
       "      <td>-19.5334</td>\n",
       "      <td>0.6812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93_23</th>\n",
       "      <td>563</td>\n",
       "      <td>-0.0238</td>\n",
       "      <td>-13.3827</td>\n",
       "      <td>0.6980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93_40</th>\n",
       "      <td>621</td>\n",
       "      <td>-0.0146</td>\n",
       "      <td>-9.0912</td>\n",
       "      <td>0.6651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93_42</th>\n",
       "      <td>259</td>\n",
       "      <td>-0.0401</td>\n",
       "      <td>-10.3983</td>\n",
       "      <td>0.7413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93_51</th>\n",
       "      <td>305</td>\n",
       "      <td>-0.0785</td>\n",
       "      <td>-23.9460</td>\n",
       "      <td>0.7279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93_57</th>\n",
       "      <td>208</td>\n",
       "      <td>-0.0297</td>\n",
       "      <td>-6.1840</td>\n",
       "      <td>0.6731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93_59</th>\n",
       "      <td>255</td>\n",
       "      <td>-0.0382</td>\n",
       "      <td>-9.7367</td>\n",
       "      <td>0.7333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>367 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        lcs     lms      lps     lrs\n",
       "54_32   365 -0.0525 -19.1550  0.7096\n",
       "54_35   629 -0.0248 -15.6015  0.6979\n",
       "54_38   947 -0.0195 -18.5037  0.6674\n",
       "54_47   532 -0.0417 -22.1767  0.6579\n",
       "54_48   663 -0.0184 -12.2099  0.6682\n",
       "54_53   796 -0.0335 -26.6577  0.7211\n",
       "55_1    583 -0.0270 -15.7313  0.6998\n",
       "55_8   1246 -0.0214 -26.6566  0.6790\n",
       "55_18   429 -0.0209  -8.9820  0.6667\n",
       "55_23  1054 -0.0168 -17.7548  0.6689\n",
       "...     ...     ...      ...     ...\n",
       "92_57   172 -0.0222  -3.8194  0.6802\n",
       "92_59   245 -0.0377  -9.2387  0.7265\n",
       "93_1    546 -0.0239 -13.0463  0.6703\n",
       "93_8    850 -0.0230 -19.5334  0.6812\n",
       "93_23   563 -0.0238 -13.3827  0.6980\n",
       "93_40   621 -0.0146  -9.0912  0.6651\n",
       "93_42   259 -0.0401 -10.3983  0.7413\n",
       "93_51   305 -0.0785 -23.9460  0.7279\n",
       "93_57   208 -0.0297  -6.1840  0.6731\n",
       "93_59   255 -0.0382  -9.7367  0.7333\n",
       "\n",
       "[367 rows x 4 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llps = ump_deg.cprs[(ump_deg.cprs['lps'] <= brust_min[0]) & (ump_deg.cprs['lms'] <= brust_min[1] )& (ump_deg.cprs['lrs'] >=brust_min[2])]\n",
    "llps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "拦截的交易中正确拦截比例:0.651\n",
      "训练集中拦截生效后可提升比例:0.053\n",
      "训练集中拦截生效数量9720， 训练集中拦截生效占总训练集比例17.601%\n"
     ]
    },
    {
     "data": {
      "image/png": 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LAQCIWh6vT4cqmnS8tlWSdN7UAosrGrjK+jarSwCAuEV3NQBAVPJ4fbr3sVVq7fBIkpIT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15f446a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ump_deg.choose_cprs_component(llps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "please wait! dump_pickle....: /Users/Bailey/abu/data/ump/ump_main_hs_deg_main\n"
     ]
    }
   ],
   "source": [
    "ump_deg.dump_clf(llps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11.2.3 跳空主裁\n",
    "\n",
    "请对照阅读ABU量化系统使用文档 ：第16节 UMP主裁交易决策 中相关内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from abupy import AbuUmpMainJump\n",
    "# 耗时操作，大概需要10几分钟，具体根据电脑性能，cpu情况\n",
    "ump_jump = AbuUmpMainJump.ump_main_clf_dump(orders_pd_train, save_order=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>buy_diff_up_days</th>\n",
       "      <th>buy_jump_down_power</th>\n",
       "      <th>buy_diff_down_days</th>\n",
       "      <th>buy_jump_up_power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>244</td>\n",
       "      <td>-1.269</td>\n",
       "      <td>155</td>\n",
       "      <td>2.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.088</td>\n",
       "      <td>171</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.159</td>\n",
       "      <td>170</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.327</td>\n",
       "      <td>162</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            result  buy_diff_up_days  buy_jump_down_power  buy_diff_down_days  \\\n",
       "2012-10-12       0               244               -1.269                 155   \n",
       "2012-10-12       0                 0               -1.088                 171   \n",
       "2012-10-12       0                 0               -1.159                 170   \n",
       "2012-10-12       1                 0               -1.327                 162   \n",
       "2012-10-12       0                 0                0.000                   0   \n",
       "\n",
       "            buy_jump_up_power  \n",
       "2012-10-12               2.17  \n",
       "2012-10-12               0.00  \n",
       "2012-10-12               0.00  \n",
       "2012-10-12               0.00  \n",
       "2012-10-12               1.22  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ump_jump.fiter.df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面这个的这个拦截特征比较明显，两天前才发生向上跳空的交易："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "失败概率最大的分类簇54_39\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>buy_diff_up_days</th>\n",
       "      <th>buy_jump_down_power</th>\n",
       "      <th>buy_diff_down_days</th>\n",
       "      <th>buy_jump_up_power</th>\n",
       "      <th>ind</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-11-02</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.831</td>\n",
       "      <td>191</td>\n",
       "      <td>1.156</td>\n",
       "      <td>562</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-21</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.037</td>\n",
       "      <td>21</td>\n",
       "      <td>1.274</td>\n",
       "      <td>4931</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-04-08</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.007</td>\n",
       "      <td>154</td>\n",
       "      <td>1.319</td>\n",
       "      <td>5785</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-17</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.689</td>\n",
       "      <td>73</td>\n",
       "      <td>1.222</td>\n",
       "      <td>6807</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-23</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.094</td>\n",
       "      <td>79</td>\n",
       "      <td>1.097</td>\n",
       "      <td>7443</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-23</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.094</td>\n",
       "      <td>79</td>\n",
       "      <td>1.097</td>\n",
       "      <td>7449</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-03</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.741</td>\n",
       "      <td>53</td>\n",
       "      <td>1.096</td>\n",
       "      <td>8088</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-07</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.238</td>\n",
       "      <td>59</td>\n",
       "      <td>1.356</td>\n",
       "      <td>8269</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-13</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.255</td>\n",
       "      <td>256</td>\n",
       "      <td>1.747</td>\n",
       "      <td>8289</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-17</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.402</td>\n",
       "      <td>300</td>\n",
       "      <td>2.142</td>\n",
       "      <td>12358</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-14</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.194</td>\n",
       "      <td>41</td>\n",
       "      <td>1.306</td>\n",
       "      <td>53590</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-14</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.194</td>\n",
       "      <td>41</td>\n",
       "      <td>1.306</td>\n",
       "      <td>53592</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-17</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.363</td>\n",
       "      <td>214</td>\n",
       "      <td>1.558</td>\n",
       "      <td>53610</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.165</td>\n",
       "      <td>53</td>\n",
       "      <td>1.345</td>\n",
       "      <td>53759</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-08</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.165</td>\n",
       "      <td>53</td>\n",
       "      <td>1.345</td>\n",
       "      <td>53763</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-05</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.924</td>\n",
       "      <td>263</td>\n",
       "      <td>2.153</td>\n",
       "      <td>53920</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-05</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.461</td>\n",
       "      <td>161</td>\n",
       "      <td>1.687</td>\n",
       "      <td>53923</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-05</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.378</td>\n",
       "      <td>263</td>\n",
       "      <td>2.305</td>\n",
       "      <td>53926</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-05</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.461</td>\n",
       "      <td>161</td>\n",
       "      <td>1.687</td>\n",
       "      <td>53928</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-10</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.515</td>\n",
       "      <td>170</td>\n",
       "      <td>1.938</td>\n",
       "      <td>54932</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            result  buy_diff_up_days  buy_jump_down_power  buy_diff_down_days  \\\n",
       "2012-11-02       0                 2               -1.831                 191   \n",
       "2013-02-21       0                 2               -1.037                  21   \n",
       "2013-04-08       0                 2               -1.007                 154   \n",
       "2013-05-17       1                 2               -1.689                  73   \n",
       "2013-05-23       0                 2               -1.094                  79   \n",
       "2013-05-23       0                 2               -1.094                  79   \n",
       "2013-06-03       0                 2               -1.741                  53   \n",
       "2013-06-07       0                 2               -1.238                  59   \n",
       "2013-06-13       0                 2               -1.255                 256   \n",
       "2013-10-17       0                 2               -1.402                 300   \n",
       "...            ...               ...                  ...                 ...   \n",
       "2017-04-14       1                 2               -2.194                  41   \n",
       "2017-04-14       1                 2               -2.194                  41   \n",
       "2017-04-17       0                 2               -1.363                 214   \n",
       "2017-05-08       0                 2               -1.165                  53   \n",
       "2017-05-08       0                 2               -1.165                  53   \n",
       "2017-06-05       0                 2               -1.924                 263   \n",
       "2017-06-05       0                 2               -1.461                 161   \n",
       "2017-06-05       0                 2               -1.378                 263   \n",
       "2017-06-05       0                 2               -1.461                 161   \n",
       "2017-07-10       0                 2               -2.515                 170   \n",
       "\n",
       "            buy_jump_up_power    ind  cluster  \n",
       "2012-11-02              1.156    562       39  \n",
       "2013-02-21              1.274   4931       39  \n",
       "2013-04-08              1.319   5785       39  \n",
       "2013-05-17              1.222   6807       39  \n",
       "2013-05-23              1.097   7443       39  \n",
       "2013-05-23              1.097   7449       39  \n",
       "2013-06-03              1.096   8088       39  \n",
       "2013-06-07              1.356   8269       39  \n",
       "2013-06-13              1.747   8289       39  \n",
       "2013-10-17              2.142  12358       39  \n",
       "...                       ...    ...      ...  \n",
       "2017-04-14              1.306  53590       39  \n",
       "2017-04-14              1.306  53592       39  \n",
       "2017-04-17              1.558  53610       39  \n",
       "2017-05-08              1.345  53759       39  \n",
       "2017-05-08              1.345  53763       39  \n",
       "2017-06-05              2.153  53920       39  \n",
       "2017-06-05              1.687  53923       39  \n",
       "2017-06-05              2.305  53926       39  \n",
       "2017-06-05              1.687  53928       39  \n",
       "2017-07-10              1.938  54932       39  \n",
       "\n",
       "[92 rows x 7 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('失败概率最大的分类簇{0}'.format(ump_jump.cprs.lrs.argmax()))\n",
    "# 拿出跳空失败概率最大的分类簇\n",
    "max_failed_cluster_orders = ump_jump.nts[ump_jump.cprs.lrs.argmax()]\n",
    "# 显示失败概率最大的分类簇，表11-6所示\n",
    "max_failed_cluster_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类簇中jump_up_power平均值为1.99， 向上跳空平均天数2.00\n",
      "分类簇中jump_down_power平均值为-1.57, 向下跳空平均天数157.34\n",
      "训练数据集中jump_up_power平均值为1.26，向上跳空平均天数87.13\n",
      "训练数据集中jump_down_power平均值为-0.79, 向下跳空平均天数83.27\n"
     ]
    },
    {
     "data": {
      "image/png": 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sNJIkSZKKNTCPWo6IA4CzMnPplPkD92nPM2R9H3A8MF7POjEzs8fxJrPsAKwC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\nA7AK2BN4GvBx4F7gKuCn9Wq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xE7gbWADsBq7MzK3dDEySpFYmOg5FxMeArwF7gbWZeW8t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\n+/X2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14846fd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.show_orders_hist(max_failed_cluster_orders, feature_columns=['buy_diff_up_days', 'buy_jump_up_power', \n",
    "                                                                'buy_diff_down_days', 'buy_jump_down_power'])\n",
    "\n",
    "print('分类簇中jump_up_power平均值为{0:.2f}， 向上跳空平均天数{1:.2f}'.format(\n",
    "    max_failed_cluster_orders.buy_jump_up_power.mean(), max_failed_cluster_orders.buy_diff_up_days.mean()))\n",
    "\n",
    "print('分类簇中jump_down_power平均值为{0:.2f}, 向下跳空平均天数{1:.2f}'.format(\n",
    "    max_failed_cluster_orders.buy_jump_down_power.mean(), max_failed_cluster_orders.buy_diff_down_days.mean()))\n",
    "\n",
    "print('训练数据集中jump_up_power平均值为{0:.2f}，向上跳空平均天数{1:.2f}'.format(\n",
    "    orders_pd_train.buy_jump_up_power.mean(), orders_pd_train.buy_diff_up_days.mean()))\n",
    "\n",
    "print('训练数据集中jump_down_power平均值为{0:.2f}, 向下跳空平均天数{1:.2f}'.format(\n",
    "    orders_pd_train.buy_jump_down_power.mean(), orders_pd_train.buy_diff_down_days.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11.2.4 价格主裁\n",
    "\n",
    "请对照阅读ABU量化系统使用文档 ：第16节 UMP主裁交易决策 中相关内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pid:21557 gmm fit:100.0%\n",
      "pid:21555 done!\n",
      "pid:21556 done!\n",
      "pid:21557 done!\n",
      "please wait! dump_pickle....: /Users/Bailey/abu/data/ump/ump_main_hs_price_main\n"
     ]
    }
   ],
   "source": [
    "from abupy import AbuUmpMainPrice\n",
    "ump_price = AbuUmpMainPrice.ump_main_clf_dump(orders_pd_train, save_order=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>buy_price_rank120</th>\n",
       "      <th>buy_price_rank90</th>\n",
       "      <th>buy_price_rank60</th>\n",
       "      <th>buy_price_rank252</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>1</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.711</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            result  buy_price_rank120  buy_price_rank90  buy_price_rank60  \\\n",
       "2012-10-12       0              1.000             1.000             1.000   \n",
       "2012-10-12       0              1.000             1.000             1.000   \n",
       "2012-10-12       0              0.996             0.994             0.992   \n",
       "2012-10-12       1              1.000             1.000             1.000   \n",
       "2012-10-12       0              0.533             0.711             1.000   \n",
       "\n",
       "            buy_price_rank252  \n",
       "2012-10-12              1.000  \n",
       "2012-10-12              1.000  \n",
       "2012-10-12              0.788  \n",
       "2012-10-12              1.000  \n",
       "2012-10-12              0.393  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ump_price.fiter.df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "失败概率最大的分类簇93_66\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>buy_price_rank120</th>\n",
       "      <th>buy_price_rank90</th>\n",
       "      <th>buy_price_rank60</th>\n",
       "      <th>buy_price_rank252</th>\n",
       "      <th>ind</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-28</th>\n",
       "      <td>1</td>\n",
       "      <td>0.562</td>\n",
       "      <td>0.717</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.351</td>\n",
       "      <td>4481</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-02-18</th>\n",
       "      <td>0</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.689</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.312</td>\n",
       "      <td>4809</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-04</th>\n",
       "      <td>1</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.383</td>\n",
       "      <td>10839</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-09</th>\n",
       "      <td>0</td>\n",
       "      <td>0.512</td>\n",
       "      <td>0.678</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.375</td>\n",
       "      <td>11065</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-12</th>\n",
       "      <td>1</td>\n",
       "      <td>0.504</td>\n",
       "      <td>0.667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.296</td>\n",
       "      <td>11478</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-12</th>\n",
       "      <td>0</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.689</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.371</td>\n",
       "      <td>11500</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-12</th>\n",
       "      <td>0</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.387</td>\n",
       "      <td>11513</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-12</th>\n",
       "      <td>0</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.387</td>\n",
       "      <td>11515</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-13</th>\n",
       "      <td>0</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.722</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.429</td>\n",
       "      <td>11530</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-13</th>\n",
       "      <td>0</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.733</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.397</td>\n",
       "      <td>11544</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-15</th>\n",
       "      <td>0</td>\n",
       "      <td>0.542</td>\n",
       "      <td>0.711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.375</td>\n",
       "      <td>49387</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-06</th>\n",
       "      <td>0</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.345</td>\n",
       "      <td>52143</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-06</th>\n",
       "      <td>0</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.345</td>\n",
       "      <td>52175</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-14</th>\n",
       "      <td>0</td>\n",
       "      <td>0.567</td>\n",
       "      <td>0.733</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.379</td>\n",
       "      <td>52456</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-14</th>\n",
       "      <td>0</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.351</td>\n",
       "      <td>52495</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-15</th>\n",
       "      <td>0</td>\n",
       "      <td>0.542</td>\n",
       "      <td>0.700</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.440</td>\n",
       "      <td>52581</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-16</th>\n",
       "      <td>0</td>\n",
       "      <td>0.542</td>\n",
       "      <td>0.689</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.437</td>\n",
       "      <td>52646</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-16</th>\n",
       "      <td>0</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.678</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.363</td>\n",
       "      <td>52651</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-20</th>\n",
       "      <td>0</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.290</td>\n",
       "      <td>54143</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-10</th>\n",
       "      <td>0</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.389</td>\n",
       "      <td>54942</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            result  buy_price_rank120  buy_price_rank90  buy_price_rank60  \\\n",
       "2013-01-28       1              0.562             0.717               1.0   \n",
       "2013-02-18       0              0.525             0.689               1.0   \n",
       "2013-09-04       1              0.521             0.667               1.0   \n",
       "2013-09-09       0              0.512             0.678               1.0   \n",
       "2013-09-12       1              0.504             0.667               1.0   \n",
       "2013-09-12       0              0.525             0.689               1.0   \n",
       "2013-09-12       0              0.529             0.667               1.0   \n",
       "2013-09-12       0              0.529             0.667               1.0   \n",
       "2013-09-13       0              0.571             0.722               1.0   \n",
       "2013-09-13       0              0.575             0.733               1.0   \n",
       "...            ...                ...               ...               ...   \n",
       "2016-11-15       0              0.542             0.711               1.0   \n",
       "2017-03-06       0              0.508             0.667               1.0   \n",
       "2017-03-06       0              0.508             0.667               1.0   \n",
       "2017-03-14       0              0.567             0.733               1.0   \n",
       "2017-03-14       0              0.533             0.667               1.0   \n",
       "2017-03-15       0              0.542             0.700               1.0   \n",
       "2017-03-16       0              0.542             0.689               1.0   \n",
       "2017-03-16       0              0.521             0.678               1.0   \n",
       "2017-06-20       0              0.508             0.667               1.0   \n",
       "2017-07-10       0              0.508             0.667               1.0   \n",
       "\n",
       "            buy_price_rank252    ind  cluster  \n",
       "2013-01-28              0.351   4481       66  \n",
       "2013-02-18              0.312   4809       66  \n",
       "2013-09-04              0.383  10839       66  \n",
       "2013-09-09              0.375  11065       66  \n",
       "2013-09-12              0.296  11478       66  \n",
       "2013-09-12              0.371  11500       66  \n",
       "2013-09-12              0.387  11513       66  \n",
       "2013-09-12              0.387  11515       66  \n",
       "2013-09-13              0.429  11530       66  \n",
       "2013-09-13              0.397  11544       66  \n",
       "...                       ...    ...      ...  \n",
       "2016-11-15              0.375  49387       66  \n",
       "2017-03-06              0.345  52143       66  \n",
       "2017-03-06              0.345  52175       66  \n",
       "2017-03-14              0.379  52456       66  \n",
       "2017-03-14              0.351  52495       66  \n",
       "2017-03-15              0.440  52581       66  \n",
       "2017-03-16              0.437  52646       66  \n",
       "2017-03-16              0.363  52651       66  \n",
       "2017-06-20              0.290  54143       66  \n",
       "2017-07-10              0.389  54942       66  \n",
       "\n",
       "[125 rows x 7 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('失败概率最大的分类簇{0}'.format(ump_price.cprs.lrs.argmax()))\n",
    "\n",
    "# 拿出价格失败概率最大的分类簇\n",
    "max_failed_cluster_orders = ump_price.nts[ump_price.cprs.lrs.argmax()]\n",
    "# 表11-8所示\n",
    "max_failed_cluster_orders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11.2.5 波动主裁\n",
    "\n",
    "请对照阅读ABU量化系统使用文档 ：第16节 UMP主裁交易决策 中相关内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pid:21599 gmm fit:100.0%\n",
      "pid:21596 done!\n",
      "pid:21597 done!\n",
      "pid:21598 done!\n",
      "pid:21599 done!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "AbuUmpMainWave: cprs shape < 50!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "please wait! dump_pickle....: /Users/Bailey/abu/data/ump/ump_main_hs_wave_main\n"
     ]
    }
   ],
   "source": [
    "from abupy import AbuUmpMainWave\n",
    "ump_wave = AbuUmpMainWave.ump_main_clf_dump(orders_pd_train, save_order=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>buy_wave_score1</th>\n",
       "      <th>buy_wave_score2</th>\n",
       "      <th>buy_wave_score3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.779</td>\n",
       "      <td>0.812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>0.027</td>\n",
       "      <td>-0.237</td>\n",
       "      <td>-0.281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>1.861</td>\n",
       "      <td>1.779</td>\n",
       "      <td>1.664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.150</td>\n",
       "      <td>-0.166</td>\n",
       "      <td>-0.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>0.121</td>\n",
       "      <td>-0.053</td>\n",
       "      <td>-0.115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            result  buy_wave_score1  buy_wave_score2  buy_wave_score3\n",
       "2012-10-12       0            0.832            0.779            0.812\n",
       "2012-10-12       0            0.027           -0.237           -0.281\n",
       "2012-10-12       0            1.861            1.779            1.664\n",
       "2012-10-12       1           -0.150           -0.166           -0.088\n",
       "2012-10-12       0            0.121           -0.053           -0.115"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ump_wave.fiter.df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "失败概率最大的分类簇78_7\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>buy_wave_score1</th>\n",
       "      <th>buy_wave_score2</th>\n",
       "      <th>buy_wave_score3</th>\n",
       "      <th>ind</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-10-19</th>\n",
       "      <td>0</td>\n",
       "      <td>2.784</td>\n",
       "      <td>2.730</td>\n",
       "      <td>2.571</td>\n",
       "      <td>303</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-22</th>\n",
       "      <td>0</td>\n",
       "      <td>2.326</td>\n",
       "      <td>2.219</td>\n",
       "      <td>2.147</td>\n",
       "      <td>5512</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-22</th>\n",
       "      <td>0</td>\n",
       "      <td>2.326</td>\n",
       "      <td>2.219</td>\n",
       "      <td>2.147</td>\n",
       "      <td>5514</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-25</th>\n",
       "      <td>0</td>\n",
       "      <td>2.722</td>\n",
       "      <td>2.678</td>\n",
       "      <td>2.542</td>\n",
       "      <td>5569</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-13</th>\n",
       "      <td>1</td>\n",
       "      <td>2.336</td>\n",
       "      <td>2.351</td>\n",
       "      <td>2.323</td>\n",
       "      <td>6503</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-07-18</th>\n",
       "      <td>1</td>\n",
       "      <td>2.716</td>\n",
       "      <td>2.640</td>\n",
       "      <td>2.482</td>\n",
       "      <td>8567</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-20</th>\n",
       "      <td>0</td>\n",
       "      <td>2.382</td>\n",
       "      <td>2.272</td>\n",
       "      <td>2.186</td>\n",
       "      <td>9748</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-28</th>\n",
       "      <td>1</td>\n",
       "      <td>2.321</td>\n",
       "      <td>2.228</td>\n",
       "      <td>2.136</td>\n",
       "      <td>10336</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-02</th>\n",
       "      <td>1</td>\n",
       "      <td>2.456</td>\n",
       "      <td>2.340</td>\n",
       "      <td>2.238</td>\n",
       "      <td>10636</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-10</th>\n",
       "      <td>0</td>\n",
       "      <td>2.488</td>\n",
       "      <td>2.525</td>\n",
       "      <td>2.504</td>\n",
       "      <td>11187</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-12</th>\n",
       "      <td>0</td>\n",
       "      <td>2.363</td>\n",
       "      <td>2.323</td>\n",
       "      <td>2.233</td>\n",
       "      <td>34967</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-15</th>\n",
       "      <td>0</td>\n",
       "      <td>2.590</td>\n",
       "      <td>2.565</td>\n",
       "      <td>2.500</td>\n",
       "      <td>34980</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-02</th>\n",
       "      <td>1</td>\n",
       "      <td>2.685</td>\n",
       "      <td>2.719</td>\n",
       "      <td>2.668</td>\n",
       "      <td>35269</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-20</th>\n",
       "      <td>0</td>\n",
       "      <td>2.480</td>\n",
       "      <td>2.443</td>\n",
       "      <td>2.353</td>\n",
       "      <td>35281</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-24</th>\n",
       "      <td>1</td>\n",
       "      <td>2.478</td>\n",
       "      <td>2.434</td>\n",
       "      <td>2.366</td>\n",
       "      <td>35294</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-24</th>\n",
       "      <td>0</td>\n",
       "      <td>2.434</td>\n",
       "      <td>2.428</td>\n",
       "      <td>2.397</td>\n",
       "      <td>35299</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-17</th>\n",
       "      <td>0</td>\n",
       "      <td>2.662</td>\n",
       "      <td>2.454</td>\n",
       "      <td>2.219</td>\n",
       "      <td>53610</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-10</th>\n",
       "      <td>0</td>\n",
       "      <td>2.564</td>\n",
       "      <td>2.390</td>\n",
       "      <td>2.176</td>\n",
       "      <td>53772</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-05</th>\n",
       "      <td>0</td>\n",
       "      <td>2.337</td>\n",
       "      <td>2.383</td>\n",
       "      <td>2.361</td>\n",
       "      <td>53924</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-11</th>\n",
       "      <td>1</td>\n",
       "      <td>2.358</td>\n",
       "      <td>2.268</td>\n",
       "      <td>2.192</td>\n",
       "      <td>54970</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            result  buy_wave_score1  buy_wave_score2  buy_wave_score3    ind  \\\n",
       "2012-10-19       0            2.784            2.730            2.571    303   \n",
       "2013-03-22       0            2.326            2.219            2.147   5512   \n",
       "2013-03-22       0            2.326            2.219            2.147   5514   \n",
       "2013-03-25       0            2.722            2.678            2.542   5569   \n",
       "2013-05-13       1            2.336            2.351            2.323   6503   \n",
       "2013-07-18       1            2.716            2.640            2.482   8567   \n",
       "2013-08-20       0            2.382            2.272            2.186   9748   \n",
       "2013-08-28       1            2.321            2.228            2.136  10336   \n",
       "2013-09-02       1            2.456            2.340            2.238  10636   \n",
       "2013-09-10       0            2.488            2.525            2.504  11187   \n",
       "...            ...              ...              ...              ...    ...   \n",
       "2015-06-12       0            2.363            2.323            2.233  34967   \n",
       "2015-06-15       0            2.590            2.565            2.500  34980   \n",
       "2015-07-02       1            2.685            2.719            2.668  35269   \n",
       "2015-07-20       0            2.480            2.443            2.353  35281   \n",
       "2015-07-24       1            2.478            2.434            2.366  35294   \n",
       "2015-07-24       0            2.434            2.428            2.397  35299   \n",
       "2017-04-17       0            2.662            2.454            2.219  53610   \n",
       "2017-05-10       0            2.564            2.390            2.176  53772   \n",
       "2017-06-05       0            2.337            2.383            2.361  53924   \n",
       "2017-07-11       1            2.358            2.268            2.192  54970   \n",
       "\n",
       "            cluster  \n",
       "2012-10-19        7  \n",
       "2013-03-22        7  \n",
       "2013-03-22        7  \n",
       "2013-03-25        7  \n",
       "2013-05-13        7  \n",
       "2013-07-18        7  \n",
       "2013-08-20        7  \n",
       "2013-08-28        7  \n",
       "2013-09-02        7  \n",
       "2013-09-10        7  \n",
       "...             ...  \n",
       "2015-06-12        7  \n",
       "2015-06-15        7  \n",
       "2015-07-02        7  \n",
       "2015-07-20        7  \n",
       "2015-07-24        7  \n",
       "2015-07-24        7  \n",
       "2017-04-17        7  \n",
       "2017-05-10        7  \n",
       "2017-06-05        7  \n",
       "2017-07-11        7  \n",
       "\n",
       "[69 rows x 6 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('失败概率最大的分类簇{0}'.format(ump_wave.cprs.lrs.argmax()))\n",
    "# 拿出波动特征失败概率最大的分类簇\n",
    "max_failed_cluster_orders = ump_wave.nts[ump_wave.cprs.lrs.argmax()]\n",
    "# 表11-10所示\n",
    "max_failed_cluster_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类簇中wave_score1平均值为2.50\n",
      "分类簇中wave_score3平均值为2.38\n",
      "训练数据集中wave_score1平均值为0.51\n",
      "训练数据集中wave_score3平均值为0.51\n"
     ]
    },
    {
     "data": {
      "image/png": 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0pQUAABjayqIDAGzVJZdft9D7v+KyixZ6/wBwprLSAgAADE1pAQAAhqa0AAAAQ1NaAACA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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15a239cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x15fd10ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.show_orders_hist(max_failed_cluster_orders, feature_columns=['buy_wave_score1', 'buy_wave_score3'])\n",
    "\n",
    "print('分类簇中wave_score1平均值为{0:.2f}'.format(\n",
    "    max_failed_cluster_orders.buy_wave_score1.mean()))\n",
    "\n",
    "print('分类簇中wave_score3平均值为{0:.2f}'.format(\n",
    "    max_failed_cluster_orders.buy_wave_score3.mean()))\n",
    "\n",
    "ml.show_orders_hist(orders_pd_train, feature_columns=['buy_wave_score1', 'buy_wave_score1'])\n",
    "\n",
    "print('训练数据集中wave_score1平均值为{0:.2f}'.format(\n",
    "    orders_pd_train.buy_wave_score1.mean()))\n",
    "\n",
    "print('训练数据集中wave_score3平均值为{0:.2f}'.format(\n",
    "    orders_pd_train.buy_wave_score1.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11.2.6 验证主裁是否称职\n",
    "\n",
    "\n",
    "请对照阅读ABU量化系统使用文档 ：第21节 A股UMP决策 中相关内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 选取有交易结果的数据order_has_result\n",
    "order_has_result = abu_result_tuple_test.orders_pd[abu_result_tuple_test.orders_pd.result != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "mean_hit_failed = 4.0\n"
     ]
    }
   ],
   "source": [
    "ump_wave.best_hit_cnt_info(ump_wave.llps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from abupy import AbuUmpMainDeg, AbuUmpMainJump, AbuUmpMainPrice, AbuUmpMainWave\n",
    "ump_deg = AbuUmpMainDeg(predict=True)\n",
    "ump_jump = AbuUmpMainJump(predict=True)\n",
    "ump_price = AbuUmpMainPrice(predict=True)\n",
    "ump_wave = AbuUmpMainWave(predict=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pid:21667 ump_deg complete:99.67%\n",
      "pid:21667 done!\n"
     ]
    }
   ],
   "source": [
    "def apply_ml_features_ump(order, predicter, progress, need_hit_cnt):\n",
    "    if not isinstance(order.ml_features, dict):\n",
    "        import ast\n",
    "        # 低版本pandas dict对象取出来会成为str\n",
    "        ml_features = ast.literal_eval(order.ml_features)\n",
    "    else:\n",
    "        ml_features = order.ml_features\n",
    "    progress.show()\n",
    "    # 将交易单中的买入时刻特征传递给ump主裁决策器，让每一个主裁来决策是否进行拦截\n",
    "    return predicter.predict_kwargs(need_hit_cnt=need_hit_cnt, **ml_features)\n",
    "\n",
    "def pararllel_func(ump, ump_name):\n",
    "    with AbuMulPidProgress(len(order_has_result), '{} complete'.format(ump_name)) as progress:\n",
    "        # 启动多进程进度条，对order_has_result进行apply\n",
    "        ump_result = order_has_result.apply(apply_ml_features_ump, axis=1, args=(ump, progress, 2,))\n",
    "    return ump_name, ump_result\n",
    "\n",
    "# 并行处理4个主裁，每一个主裁启动一个进程进行拦截决策\n",
    "parallel = Parallel(\n",
    "    n_jobs=4, verbose=0, pre_dispatch='2*n_jobs')\n",
    "out = parallel(delayed(pararllel_func)(ump, ump_name)\n",
    "                              for ump, ump_name in zip([ump_deg, ump_jump, ump_price, ump_wave], \n",
    "                                                       ['ump_deg', 'ump_jump', 'ump_price', 'ump_wave']))\n",
    "\n",
    "# 将每一个进程中的裁判的拦截决策进行汇总\n",
    "for sub_out in out:\n",
    "    order_has_result[sub_out[0]] = sub_out[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "四个裁判整体拦截正确率63.33%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ump_deg</th>\n",
       "      <th>ump_jump</th>\n",
       "      <th>ump_price</th>\n",
       "      <th>ump_wave</th>\n",
       "      <th>sum_bk</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07-20</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-21</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-03</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ump_deg  ump_jump  ump_price  ump_wave  sum_bk  result\n",
       "2017-07-20        1         0          1         0       2      -1\n",
       "2017-07-21        0         0          1         0       1      -1\n",
       "2017-07-21        0         0          1         0       1      -1\n",
       "2017-08-01        0         0          1         0       1      -1\n",
       "2017-08-03        0         0          1         0       1      -1"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "block_pd = order_has_result.filter(regex='^ump_*')\n",
    "# 把所有主裁的决策进行相加\n",
    "block_pd['sum_bk'] = block_pd.sum(axis=1)\n",
    "block_pd['result'] = order_has_result['result']\n",
    "# 有投票1的即会进行拦截\n",
    "block_pd = block_pd[block_pd.sum_bk > 0]\n",
    "print('四个裁判整体拦截正确率{:.2f}%'.format(block_pd[block_pd.result == -1].result.count() / block_pd.result.count() * 100))\n",
    "block_pd.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "角度裁判拦截正确率65.09%, 拦截交易数量974\n",
      "角度扩展裁判拦拦截正确率66.67%, 拦截交易数量3\n",
      "单混裁判拦截正确率67.06%, 拦截交易数量85\n",
      "价格裁判拦截正确率64.57%, 拦截交易数量796\n"
     ]
    }
   ],
   "source": [
    "print('角度裁判拦截正确率{:.2f}%, 拦截交易数量{}'.format(*sub_ump_show('ump_deg')))\n",
    "print('角度扩展裁判拦拦截正确率{:.2f}%, 拦截交易数量{}'.format(*sub_ump_show('ump_jump')))\n",
    "print('单混裁判拦截正确率{:.2f}%, 拦截交易数量{}'.format(*sub_ump_show('ump_wave')))\n",
    "print('价格裁判拦截正确率{:.2f}%, 拦截交易数量{}'.format(*sub_ump_show('ump_price')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11.2.7 在abu系统中开启主裁拦截模式\n",
    "\n",
    "请对照阅读ABU量化系统使用文档 ：第21节 A股UMP决策 中相关内容"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11.3 边裁"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11.3.1 角度边裁\n",
    "\n",
    "请对照阅读ABU量化系统使用文档 ：第17节 UMP边裁交易决策，第21节 A股UMP决策 中相关内容"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11.3.2 价格边裁\n",
    "\n",
    "请对照阅读ABU量化系统使用文档 ：第17节 UMP边裁交易决策，第21节 A股UMP决策 中相关内容"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11.3.3 波动边裁\n",
    "\n",
    "请对照阅读ABU量化系统使用文档 ：第17节 UMP边裁交易决策，第21节 A股UMP决策 中相关内容"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11.3.4 综合边裁\n",
    "\n",
    "请对照阅读ABU量化系统使用文档 ：第17节 UMP边裁交易决策，第21节 A股UMP决策 中相关内容"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11.3.5 验证边裁是否称职\n",
    "\n",
    "请对照阅读ABU量化系统使用文档 ：第21节 A股UMP决策 中相关内容"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11.3.6 在abu系统中开启边裁拦截模式\n",
    "\n",
    "请对照阅读ABU量化系统使用文档 ：第21节 A股UMP决策 中相关内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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